35 results about "Motor symptoms" patented technology

The invention relates to the technical field of medical data analysis, in particular to a spatio-temporal characteristic quantitative evaluation method for Parkinson's disease motion symptoms, which comprises the following steps: deploying an inertial measurement unit to collect three-dimensional acceleration angular velocity magnetic field data, constructing a human body connection structure to generate a connection neural network topological structure, the method comprises the following steps: calculating trajectory direction angle and angular velocity change adaptive weighting to judge stability, aggregating multiple rounds of convolution propagation of adjacent features to form a space-time fusion motion feature set, identifying tremor gait amplitude according to time sequence multi-head attention to extract a time sequence feature mode, calculating tremor gait coordination to generate a Parkinson's disease motion symptom quantitative evaluation result, and calculating a Parkinson's disease motion symptom quantitative evaluation result. According to the method, the limb coordination is captured by constructing sensor network motion data topological connection and fusing multi-dimensional part information, the remote association is captured by keeping the time sequence stable through adaptive weight attenuation and connection convolution depth aggregation features, and the multi-head attention fine recognition tremor frequency and gait change are combined. And the Parkinson's disease symptom identification and evaluation consistency is improved.

The invention relates to a Parkinson's disease motion symptom auxiliary diagnosis system based on multi-modal artificial intelligence, and belongs to the field of medical AI auxiliary diagnosis. The system integrates multi-modal image data, and realizes common symptom classification and severity degree scoring of four motion symptoms of Parkinson's disease through preprocessing, multi-modal feature extraction combined with a 3D CNN branch and a Vision Transform branch, causal feature screening based on an IAMB algorithm, and a multi-modal fusion and multi-task learning module. The system can effectively fuse structure and functional image information, improves diagnosis accuracy and interpretability, is especially suitable for early symptom identification and quantitative evaluation, and has high clinical practical value.

The invention relates to a novel compound as a 5-HT2A receptor inverse agonist, and a preparation method and a pharmaceutical composition thereof. The invention also relates to application of the compound or the pharmaceutical composition in preparation of drugs for treating 5-HT2A receptor related diseases, wherein the diseases comprise non-motor symptoms caused by Parkinson's disease, such as delusional disorder, hallusion, depression, anxiety, cognitive disorder and sleep disorder; dementia-related mental disorders; major depression; or negative symptoms of schizophrenia and the like.

To provide a walking evaluation support method, a walking evaluation support device and a walking evaluation support program capable of confirming motor symptoms from multiple angles.SOLUTION: A walking evaluation support method causes a computer to: acquire and record data corresponding to the walking motion of a subject wearing a display device that displays a virtual reality (VR) environment simulating a living environment via a sensor; and reproduce walking motion corresponding to the data through the motion of an avatar in a three-dimensional space corresponding to the virtual reality environment in which a user can change his / her point of sight, in order to allow the user to evaluate the subject's walking motion.SELECTED DRAWING: Figure 5

The present invention relates to a novel compound as a 5-HT 2A receptor inverse agonist, a method for preparing the same and a pharmaceutical composition. It also relates to the use of the compound or the pharmaceutical composition in the manufacture of a medicament for treating 5-HT 2A receptor related diseases, including non-motor symptoms of Parkinson's disease: delusions, hallucinations, depression, anxiety, cognitive impairment, sleep disorders; dementia-related psychiatric disorders; major depressive disorder; or negative symptoms of schizophrenia, etc.

The present application relates to the technical field of medical data analysis, in particular to a spatiotemporal feature quantitative evaluation method for Parkinson's disease motor symptoms, comprising: deploying an inertial measurement unit to collect three-dimensional acceleration angular velocity magnetic field data, constructing a human body connection structure to generate a connection neural network topology, calculating a trajectory direction angle and angular velocity change adaptive weighting to judge stability, aggregating adjacent features to form a multi-round convolution propagation to form a spatiotemporal fusion motion feature set, identifying tremor gait amplitude according to a time sequence multi-head attention to extract a time sequence feature mode, and calculating tremor gait coordination to generate a Parkinson's disease motor symptom quantitative evaluation result, wherein, in the present application, a sensor network motion data topology connection is constructed, multi-dimensional part information is fused to capture limb coordination, adaptive weight decay and connection convolution deep aggregation features are used to maintain time sequence stability and capture long-range associations, and multi-head attention is combined to distinguish tremor frequency and gait changes, thereby improving the consistency of Parkinson's symptom recognition and evaluation.

The invention discloses wearable multi-lead Parkinson's disease motion symptom and sleep disorder monitoring equipment and method, and aims to solve the problems that traditional equipment is single in monitoring dimension, weak in scene adaptation and missing in quantitative closed loop. The device comprises a multi-lead wearable device and a cloud intelligent analysis platform, the multi-lead wearable device comprises two-side wrist and ankle nine-axis inertial sensors (motion signals), limb / face chin muscle electromyographic electrodes, two-side outer ocular canthus electromyographic electrodes (bio-electricity signals) and a waist six-axis body position sensor (body position signals). The edge calculation unit (signal synchronization, preprocessing, feature extraction and transmission storage) is integrated with the waist base box; and the cloud end adopts a support vector machine and other algorithms for fusion analysis, and generates a symptom quantification result and a structured report containing MDS-UPDRS scores. The method comprises the steps of signal acquisition, processing and dual-channel redundancy transmission, and the symptom weight is optimized in combination with reinforcement learning, so that multi-modal synchronous acquisition, full-period monitoring and accurate evaluation associated with clinical scores are realized.

The invention relates to a novel compound as a 5-HT2A receptor inverse agonist, and a preparation method and a pharmaceutical composition thereof. The invention also relates to application of the compound or the pharmaceutical composition in preparation of drugs for treating 5-HT2A receptor related diseases, wherein the diseases comprise non-motor symptoms caused by Parkinson's disease, such as delusional disorder, hallusion, depression, anxiety, cognitive disorder and sleep disorder; dementia-related mental disorders; major depression; or negative symptoms of schizophrenia and the like.

To provide a system and method for the digital graphic measurement of Parkinson's disease. [Solution] A system and method for digital graphic measurement of Parkinson's disease according to an embodiment of the present invention are illustrated. One embodiment includes a digital graphic measurement device comprising a housing, a plurality of keys mounted on the housing, a plurality of sensors, the sensors in the plurality of sensors being associated with the keys in the plurality of keys and capable of measuring the amplitude of key presses, and an input / output interface within the housing, wherein the digital graphic measurement device is configured to transmit key press data generated by the sensors to a controller via the input / output interface, and the key press data is used to generate a plurality of digital graphic indices that describe the motor symptoms of the user of the digital graphic measurement device.

Devices, systems, software, and methods are provided for administering adaptive deep brain stimulation (DBS) to a subject who has been treated with medication and / or continuous DBS in order to relieve residual motor symptoms. In particular, DBS is performed with a neural recording device that records brain electrical signals from neural activity associated with a residual motor symptom and automatically adjusts deep brain stimulator settings and / or delivers electrical stimulation to the brain when pre-specified patterns of neural activity associated with the residual motor symptom are detected. Machine learning or linear computational models are used to detect and classify patterns of neural activity associated with the residual motor symptom.

Various embodiments of the present technology generally relate to closed loop deep brain stimulation based on inferred sleep stage from physiological data using machine learning classifiers. Some embodiments, for example, may use subthalamic nucleus (STN) deep brain stimulation (DBS) to treat advanced Parkinson's Disease motor symptoms and improve sleep by identifying sleep stages commensurate with clinician-scored polysomnography (PSG). The DBS may be adapted to include a novel artificial neural network (ANN) that triggers targeted stimulation in response to inferred sleep state from STN local field potentials (LFPs) recorded from implanted DBS electrodes. A feedforward neural network can be trained to prospectively identify sleep stage with PSG-level accuracy. In some embodiments, the machine learning model stored within the DBS may also adapt stimulation during specific sleep stages to treat targeted sleep deficits.

The invention relates to a novel compound as a 5-HT2A receptor inverse agonist, and a preparation method and a pharmaceutical composition thereof. The invention also relates to application of the compound or the pharmaceutical composition in preparation of drugs for treating 5-HT2A receptor related diseases, wherein the diseases comprise non-motor symptoms caused by Parkinson's disease, such as delusional disorder, hallusion, depression, anxiety, cognitive disorder and sleep disorder; dementia-related mental disorders; major depression; or negative symptoms of schizophrenia and the like.

The invention discloses a method and a system for comprehensively monitoring daytime and nighttime symptoms of Parkinson's disease, which are used for solving the problems of fragmentization, strong subjectivity, low calculation efficiency and the like of the existing monitoring. The system synchronously collects real-time clock, body position, EOG, EMG and IMU (inertial measurement unit) multi-mode signals, hierarchically identifies daytime activity, night sleep, pre-sleep and initial awakening stages, and then adaptively activates corresponding modules to analyze symptoms: monitoring movement symptoms and tumble in daytime and attributing, identifying sleep disorder at night, and identifying sleep disorder at night. A morning stiffness severity index is generated by fusing time, myoelectricity and motion signals in the initial awakening period; the characteristics are mapped into UPDRS / PDDS scores based on machine learning, a structured report containing visualization and historical comparison is generated, a diagnosis and treatment closed loop of'monitoring-analysis-reporting-doctor evaluation-treatment adjustment 'is constructed, objective continuous monitoring is achieved, Parkinson's disease diagnosis and treatment are assisted, and medical efficiency is improved.

The invention belongs to the technical field of medicines, and particularly relates to application of a Chinese knotweed rhizome and aloe bowel-relaxing capsule to preparation of a medicine for treating Parkinson's disease. The Chinese knotweed rhizome and aloe bowel-relaxing capsule can obviously improve the damage of nigra and striatum dopaminergic neurons of mice suffering from the Parkinson's disease complicated with depression caused by MPTP and CUMS, and inhibit the activation of microglia and astroglia cells; the traditional Chinese medicine composition not only can relieve motion symptoms of mice with Parkinson's disease, but also can remarkably improve non-motion symptoms including mental disorder of the mice with Parkinson's disease, and has a remarkable treatment effect on depression of Parkinson's disease.

The present disclosure relates to methods and compositions for treating Parkinson's disease, a neurodegenerative disorder characterized by loss of dopaminergic neurons. In particular, the present disclosure provides preparations of dopaminergic cells that have been demonstrated to have therapeutic effects on both motor and non-motor symptoms of the disease.

The invention relates to a novel compound as a 5-HT2A receptor inverse agonist, and a preparation method and a pharmaceutical composition thereof. The invention also relates to application of the compound or the pharmaceutical composition in preparation of drugs for treating 5-HT2A receptor related diseases, wherein the diseases comprise non-motor symptoms caused by Parkinson's disease, such as delusional disorder, hallusion, depression, anxiety, cognitive disorder and sleep disorder; dementia-related mental disorders; major depression; or negative symptoms of schizophrenia and the like.

The application is suitable for the field of physiological signal recognition, and provides a Parkinson motor symptom evaluation method and device, an electronic device and a storage medium. The method comprises the following steps: obtaining a motor symptom evaluation mode for evaluating the current Parkinson motor symptom of a to-be-evaluated Parkinson patient according to historical evaluation data of the to-be-evaluated Parkinson patient, the motor symptom evaluation mode comprising at least one group of motor evaluation items; collecting motor monitoring data corresponding to the motor evaluation items completed by the to-be-evaluated Parkinson patient; collecting the motor monitoring data through at least one group of wearable sensors; determining an evaluation feature combination for evaluating the current Parkinson motor symptom of the to-be-evaluated Parkinson patient according to the motor monitoring data; the evaluation feature combination comprises a plurality of motor features obtained from the motor monitoring data; inputting the evaluation feature combination into a trained multi-feature fusion classification model to obtain an evaluation result. The application can improve the accuracy of Parkinson motor symptom evaluation for Parkinson patients.

The purpose of the present invention is to provide: a therapeutic agent or a recurrence preventive agent for serotonergic system- or dopaminergic system-related diseases, in particular, mental dysfunction symptoms and other non-motor symptoms of Parkinson's disease; and a method for treating the aforesaid symptoms or preventing the recurrence of the same. A compound represented by formula (1) [wherein each symbol is as defined in the description] or a pharmaceutically acceptable salt thereof can exhibit an effect of treating serotonergic system- or dopaminergic system-related diseases, in particular, mental dysfunction symptoms and other non-motor symptoms of Parkinson's disease and / or an effect of preventing the recurrence of the same.

The invention provides a system for quantitative evaluation of Parkinson's disease hand motion symptoms, and the system comprises a glove base which is worn on the hand of a patient to be evaluated; the triboelectric sensors are located at finger joints on the back side of the glove base and used for collecting bending signals of the finger joints when the patient executes specified actions; the inertial measurement unit is positioned in a hand back area on the glove base and is used for collecting a movement signal of the whole hand when the patient executes a specified action; the data acquisition and processing unit is used for calculating a tremor evaluation index value and a muscular tension evaluation index value based on the motion signal of the whole hand, and calculating a motion delay evaluation index value based on the bending signal of the finger joint and the motion signal of the whole hand; and inputting the three types of evaluation index values into a pre-constructed mapping relationship between each evaluation index and a clinical score to obtain respective scores, and summing the scores to obtain an overall score. According to the invention, fine actions of the hand of the Parkinson's disease patient can be reflected, and accurate quantitative indexes of hand movement symptoms can be provided.

The invention relates to a gait intervention method, device and equipment for an early Parkinson's disease patient and a medium. The intervention method comprises the following steps: a gait event detection step: obtaining gait data, and obtaining a gait cycle of a PD patient by using the gait data; and a rhythm generation step: according to the gait cycle of the patient, generating rhythms conforming to the current gait cycle of the left foot and the right foot of the patient, detecting real-time gait data of the patient during current walking, and dynamically generating a new rhythm according to the gait improvement condition of the patient. Personalized rhythms can be formulated according to the conditions of the patient, the period difference between the affected side and the normal side of the patient is improved, the falling risk of the PD patient is reduced, and the exercise symptom progress is delayed.

The invention belongs to the field of neurobiology and neuroengineering, and provides a method for predicting and preventing dyskinesia diseases (such as Parkinson's disease) caused by dopamine system damage before clinical symptoms appear by taking abnormal low-frequency beta neural oscillation of a specific brain region as a biomarker. The method is used for predicting abnormal high-frequency beta neural oscillation occurring after the diseases (especially the period after motion symptoms appear); wherein the monitoring and intervention target brain region of the abnormal low-frequency beta oscillation is striatum, and the abnormal high-frequency beta oscillation is visible in the striatum, the motor cortex and the subthalamic nucleus.

The invention belongs to the technical field of medicines for resisting Parkinson's disease, and particularly relates to application of micromolecule 18: 1 LPE in preparation of medicines for preventing and / or treating Parkinson's disease. The invention particularly provides application of small molecule 18: 1 LPE in preparation of a medicine for preventing and / or treating Parkinson's disease. The micromolecule 18: 1 LPE can increase the phosphorylation level of Bcl-2 and activate an autophagy pathway, so that pathological aggregation of alpha-syn is relieved, the neuroprotective effect is achieved, and PD motion symptoms are relieved. Therefore, the small molecule 18: 1 LPE has the prospect of being developed into the medicine for preventing and / or treating the Parkinson's disease. The invention provides a new thought and scientific basis for preventing and treating neurodegenerative diseases such as Parkinson's disease by activating an autolysosome approach.

The invention discloses a Parkinson's disease patient subtype intelligent typing method based on machine learning, and the method comprises the steps: integrating the multi-dimensional data of a patient, including demographic information, motor and non-motor symptoms, iconography and biomarkers, and utilizing a technical means of combining unsupervised learning and supervised learning, and carrying out the subtype intelligent typing of the Parkinson's disease patient. The Parkinson's disease classification method has the advantages that fine layering of Parkinson's disease patients is achieved, the limitation problems of roughness and single dimension of a traditional classification method are solved, disease heterogeneity can be revealed more accurately, and a scientific basis is provided for formulating an individualized treatment strategy; the disease subtypes with different clinical features and prognosis differences are recognized through clustering analysis, then the supervised learning model is used for conducting rapid and accurate subtype classification on new patients, the classification result is integrated into a clinical decision support system, comprehensive and personalized patient management suggestions are provided for doctors, and the life quality of the patients is improved.

The invention discloses wearable multi-lead Parkinson's disease motion symptom and sleep disorder monitoring equipment and a signal processing method. The equipment comprises a head, a left upper limb, a right upper limb, a left lower limb, a right lower limb, independent waist wireless sensing nodes and a waist base box, the head node collects EOG and chin muscle sEMG signals, the upper and lower limb nodes integrate nine-axis IMU and sEMG electrodes to collect motion and electromyographic signals, the waist node collects trunk position signals, the base box achieves microsecond hardware synchronization, signal preprocessing and fusion and feature extraction of all the nodes, and local caching and self-adaptive transmission are supported when the network is abnormal. The method comprises the steps of self-inspection initialization, synchronous acquisition preprocessing, multi-modal signal matrix construction, single symptom feature vector set extraction, self-adaptive transmission and cloud analysis. The problems that existing equipment is single in monitoring, poor in environment adaptability and low in compliance are solved, and objective continuous data support is provided for clinical diagnosis and treatment adjustment of the Parkinson's disease.

The invention relates to a Parkinson's disease animal model and a construction method and application thereof. Relates to the technical field of animal model construction. The construction method comprises the steps that a heterocyclic arylamine compound is adopted for administration to animals, namely the animal model of the Parkinson's disease is constructed, and the heterocyclic arylamine compound is 2-amino-1-methyl-6-phenylimidazole [4, 5-b] pyridine. According to the construction method, 2-amino-1-methyl-6-phenylimidazole [4, 5-b] pyridine is creatively used for constructing the Parkinson's disease animal model, Parkinson's disease sample movement symptoms and nerve injury expressions can be effectively simulated in a short time, and the construction method is an efficient Parkinson's disease model construction method.

This invention proposes an early detection method for Parkinson's disease based on EfficientNetV2-S. Addressing the problems of traditional diagnosis relying on subjective assessment of motor symptoms by doctors, which is prone to misdiagnosis and missed diagnosis in the early stages, this method acquires handwritten images through spiral drawing and waveform handwriting tasks. It then utilizes EfficientNetV2-S to automatically learn subtle jitter features and motion control anomalies in the trajectory. Multi-scale feature fusion enhances the perception of minute local changes, and dual-task joint learning improves the comprehensive discrimination ability across different handwriting tasks. After automatic alignment, size normalization, and data augmentation, the data is input into the model, achieving high-precision, high-efficiency, non-invasive, and easily scalable early screening and assisted diagnosis.

The purpose of the present invention is to provide: a therapeutic agent or a recurrence preventive agent for serotonergic system- or dopaminergic system-related diseases, in particular, mental dysfunction symptoms and other non-motor symptoms of Parkinson's disease; and a method for treating the aforesaid symptoms or preventing the recurrence of the same. A compound represented by formula (1) [wherein each symbol is as defined in the description] or a pharmaceutically acceptable salt thereof can exhibit an effect of treating serotonergic system- or dopaminergic system-related diseases, in particular, mental dysfunction symptoms and other non-motor symptoms of Parkinson's disease and / or an effect of preventing the recurrence of the same.

The invention provides a method and a device for generating personalized levodopa prescription recommendation for Parkinson's disease, which are applied to the technical field of artificial intelligence, and the method comprises the following steps: carrying out virtual patient modeling based on patient data to obtain a virtual patient model, the virtual patient model is used for simulating pharmacokinetic behaviors of the levodopa preparation in the body of a patient and generating exercise symptom scores; inputting a preset medication behavior, the exercise symptom score and the patient data into the state transition model to obtain a prediction result of the symptom variation after the target time; and performing multi-stage reinforcement learning based on the patient data, the medication behavior and the prediction result of the symptom variation after the target time through the state transition model to obtain a personalized levodopa prescription strategy, the personalized levodopa prescription strategy being used for outputting levodopa prescription recommendation according to the input patient data. According to the invention, personalized and dynamic levodopa medication guidance can be provided for Parkinson's disease patients.

This invention belongs to the field of pharmaceutical technology, specifically relating to the application of Shouhui Tongbian Capsules in the preparation of drugs for treating Parkinson's disease. Shouhui Tongbian Capsules can significantly improve the damage to dopamine neurons in the substantia nigra and striatum of Parkinson's disease mice with depression induced by MPTP and CUMS, and inhibit the activation of microglia and astrocytes; it not only alleviates the motor symptoms of Parkinson's disease mice, but also significantly improves the non-motor symptoms of Parkinson's disease mice, including mental disorders, and has a significant therapeutic effect on depression caused by Parkinson's disease.

The present disclosure provides a method of treating Dementia with Lewy Bodies (DLB) in a subject, comprising administering to the subject a therapeutically effective amount of a compound of formula Compound I or a pharmaceutically acceptable salt thereof. The method improves cognition, activities of daily living, neuropsychiatric symptoms, and motor symptoms in the subject. The compound may be administered orally for about 6 months or longer, with a total daily dose of about 100 mg to about 300 mg. The method results in improvements in various assessment scales including Montreal Cognitive Assessment Scale (MOCA), Cognitive Drug Research Battery (CDR), Clinician Assessment of Fluctuation (CAF), Alzheimer's Disease Cooperative Study - Activities of Daily Living (ADCS-ADL), Neuropsychiatric Inventory (NPI), and Unified Parkinson's Disease Rating Scale (UPDRS).